Systems and methods of automatically detecting failure patterns for semiconductor wafer fabrication processes

ABSTRACT

A system and method of automatically detecting failure patterns for a semiconductor wafer process is provided. The method includes receiving a test data set collected from testing a plurality of semiconductor wafers, forming a respective wafer map for each of the wafers, determining whether each respective wafer map comprises one or more respective objects, selecting the wafer maps that are determined to comprise one or more respective objects, selecting one or more object indices for selecting a respective object in each respective selected wafer map, determining a plurality of object index values in each respective selected wafer map, selecting an object in each respective selected wafer map, determining a respective feature in each of the respective selected wafer, classifying a respective pattern for each of the respective selected wafer maps and using the respective wafer fingerprints to adjust one or more parameters of the semiconductor fabrication process.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation application of and claims priority toU.S. patent application Ser. No. 13/455,186, filed on Apr. 25, 2012, theentirety of which is herein incorporated by reference.

FIELD

The present disclosure is directed generally to semiconductor waferfabrication processes and more particularly to systems and methods toanalyze failures in semiconductor wafer fabrication processes.

DESCRIPTION OF THE RELATED ART

Integrated circuits are produced by a plurality of complex semiconductorfabrication processes in a semiconductor wafer fabrication facility.Maintaining a high yield in semiconductor fabrication processescontinues to be a critical factor in reducing the costs of devicefabrication. However, despite the sophistication of semiconductor waferfabrication facilities and expense of semiconductor manufacturing tools,achieving a high yield remains a difficult accomplishment. For example,the natural variation inherent in processes performed in semiconductorfabrication facilities, tool function changes or drift, inadequatesacrificial film removal, tool imperfections, contamination, and/or toolmiscalibration are all challenges facing semiconductor fabricationfacilities that can cause unacceptable levels of chip failures. Failureanalysis is therefore critical to solution identification and maximizingyield.

Failure analysis methods involve manual “eyeball” techniques in whichsemiconductor process engineers examine selected sample wafers, checkfor failure results and manually classify the wafers into failurepatterns. These non-systematic processes are labor-intensive,time-consuming, limited to small sub-sets of the high volume ofsemiconductor wafers that are manufactured daily and will be unable tomeet Giga Fab and future 450 mm targets.

BRIEF DESCRIPTION OF THE DRAWINGS

Various aspects of the present disclosure will be or become apparent toone with skill in the art by reference to the following detaileddescription when considered in connection with the accompanyingexemplary non-limiting embodiments.

FIG. 1 is a flow chart illustrating a method of automatically detectingfailure patterns for a semiconductor wafer fabrication process accordingto embodiments of the present disclosure.

FIG. 2 is a flow chart illustrating the step of selecting an object in amethod of automatically detecting failure patterns for a semiconductorwafer fabrication process according to some embodiments.

FIGS. 3A-3D are simplified diagrams of wafer maps according to variousembodiments of the present disclosure.

FIGS. 3E and 3F illustrate two diagrams showing a step of featuredetermination using a pair of object indices and stored featureinformation according to embodiments of the present disclosure.

FIG. 4A is a simplified diagram of a wafer map according to variousembodiments.

FIG. 4B illustrates a simplified chart of a plurality of object indexvalues for according to various embodiments.

FIG. 4C illustrates a diagram showing a step of feature determinationusing a pair of object indices and stored feature information accordingto embodiments of the present disclosure.

FIG. 5 are simplified diagrams showing stored pattern informationaccording to some embodiments.

FIG. 6 is a diagram showing a step of pattern determination according toembodiments of the present disclosure.

FIG. 7 is a flow chart illustrating a step of feature determinationusing a pair of object indices and stored feature information accordingto embodiments of the present disclosure.

FIG. 8 is a diagram showing a step of new feature determination using apair of object indices and stored feature information according toembodiments of the present disclosure.

FIG. 9 is a flow chart illustrating a method of automatically detectingfailure patterns for a semiconductor wafer fabrication process accordingto some embodiments.

FIG. 10 is a diagram of an illustrative example of an architecture of acomputer processing unit according to some embodiments.

FIG. 11 is a block diagram of a processor system for performing a methoddescribed herein.

FIG. 12 is an example of a system of automatically detecting failurepatterns for a semiconductor wafer fabrication process according toembodiments of the present disclosure.

DETAILED DESCRIPTION OF THE EXAMPLES

With reference to the Figures, where like elements have been given likenumerical designations to facilitate an understanding of the drawings,the various embodiments of a system and a method of automaticallydetecting failure patterns for a semiconductor wafer fabrication processare described. The figures are not drawn to scale.

The following description is provided as an enabling teaching of arepresentative set of examples. Those skilled in the art will recognizethat many changes can be made to the embodiments described herein whilestill obtaining beneficial results. It will also be apparent that someof the desired benefits discussed below can be obtained by selectingsome of the features or steps discussed herein without utilizing otherfeatures or steps. Accordingly, those who work in the art will recognizethat many modifications and adaptations, as well as subsets of thefeatures and steps described herein are possible and can even bedesirable in certain circumstances. Thus, the following description isprovided as illustrative and is not limiting.

This description of illustrative embodiments is intended to be read inconnection with the accompanying drawings, which are to be consideredpart of the entire written description. In the description ofembodiments disclosed herein, any reference to direction or orientationis merely intended for convenience of description and is not intended inany way to limit the scope of the disclosure. Relative terms such as“lower,” “upper,” “horizontal,” “vertical,”, “above,” “below,” “up,”“down,” “top,” “normal” and “bottom” as well as derivative thereof(e.g., “horizontally,” “downwardly,” “upwardly,” etc.) should beconstrued to refer to the orientation as then described or as shown inthe drawing under discussion. These relative terms are for convenienceof description only and do not require that the apparatus be constructedor operated in a particular orientation. Moreover, various features andbenefits are illustrated by reference to the exemplary embodiments.Accordingly, the subject matter of this disclosure and the appendedclaims are expressly not limited to such exemplary embodiments.

As used herein, use of a singular article such as “a,” “an” and “the”does not exclude pluralities of the article's object unless the contextclearly and unambiguously dictates otherwise.

Failure pattern recognition at various stages of wafer fabricationprocessing, handling and transfer is used for rapid semiconductorfabrication problem identification and to maximizing yield Improvedsystems and methods of automatically detecting failure patterns for asemiconductor wafer fabrication process are provided. The systems andprocesses described herein are not limited to any specific semiconductorprocess technology, production area, equipment, technology node,fabrication tool, wafer size, etc. The processes described herein can beperformed by apparatus including among its components any suitablecommercially available equipment commonly used in the art to fabricatesemiconductor wafers, or alternatively, using future developedequipment. The inventors have developed systems and methods ofautomatically detecting failure patterns determined at various stages ofwafer transfer, handling and processing for a semiconductor waferfabrication process to improve process quality, and yields and reducecosts associated with failure pattern recognition.

The inventors have discovered a fast and reliable method and system forautomatically classifying and clustering failure patterns that providesaccurate failure pattern recognition and tunable pattern ranking. Theinventors have observed that a combination of knowledge and statisticalbased algorithms can be utilized to automatically detect and recognizefailure patterns for collected failure test data. The inventors havedeveloped a system and method that is adaptive and self-learning in thatas more failure data are received into the system, more refined failurefeatures and patterns can be determined and new features and patternscan be formed. Thus, the system and method will continue to provideimproved accuracy in failure pattern recognition as more failure data isanalyzed and the library of wafer fingerprints is further developed. Theinventors have also determined a reliable method and system ofautomatically determining the strength of correlation of new featuresand patterns in a tunable ranking system.

FIG. 1 is a flow chart illustrating a method 100 of automaticallydetecting failure patterns for a semiconductor wafer fabricationprocess. At block 102, a plurality of semiconductor wafers arefabricated. A variety of test data is collected from fabricated wafersthat have completed production as illustrated at blocks 104 and 106. Atblock 104, circuit performance (CP) tests, in-line defect tests, bintests, wafer acceptance tests (WAT or electrical test method) and/ordefect tests can be performed by applying the appropriate test criteriato the fabricated wafers. For example, a defect map can be formed usingKLA-Tencor equipment. At block 106, a test data set is collected fromtesting the fabricated wafers at block 104. At block 105, a respectivewafer map is formed for each of the wafers based on the collected testdata set. For example, during the fabrication process of the pluralityof semiconductor wafers, various in-process layers of each of theplurality of wafers can be verified by an in-line defect test to collecta test data set. A respective wafer map can be formed for each waferusing the test data collected and/or measured at various points onvarious in-line process layers (e.g. film deposition layers, lithographylayers, etching layers, diffusion layers) of the wafer. In someembodiments, wafer maps can be generated based on a test data setmeasured at various points at each of the various in-process layersduring fabrication of each of the plurality of wafers. The measurementpoints can be organized to form a wafer map.

At block 110, in various embodiments, the method determines whether eachrespective wafer map includes one or more respective objects. An object,as defined herein, is a collection of test data on a wafer map formedfor a respective wafer that is indicative of wafer failure. In variousembodiments, an object can be a collection of test data formed by two ormore adjacent failure die. In some embodiments, the step of determiningwhether each respective wafer map includes one or more respectiveobjects can include discriminating failure data from noise on eachrespective wafer map. For example, a k-nearest neighbor algorithm may beused in discriminating objects from noise and in separating the failuredata on the wafer map into discrete objects. Further by way of example,a linear discriminant analysis (LDA) can be performed to discriminatefailure data from noise and to separate the failure data on the wafermap into discrete objects. At block 112, if no objects are determined tobe present on a wafer map, then the wafer is determined to be suitablefor use and no further processing is performed on the wafer map. In someembodiments, the non-defect wafer maps can be discarded. In otherembodiments, a respective wafer map is only generated if test data for arespective wafer includes failure data forming one or more respectiveobjects. At block 110, if a respective wafer map is determined toinclude one or more respective objects, the respective wafer map isselected for further processing.

In various embodiments, a respective wafer map can be determined toinclude one object. In other embodiments, a respective wafer map can bedetermined to include two or more objects. If the wafer map isdetermined to include more than one object, then the wafer map can besegmented by object where each object in the wafer map is individuallyprocessed as described below. In some embodiments, a wafer map can besplit into several sub-wafer maps, each containing a subset of thefailure data for a particular wafer map.

At block 115, one or more object indices for selecting a respectiveobject in each respective selected wafer map of the plurality of wafermaps are selected. As used herein, an object index for selecting anobject on a wafer map is a mechanism for describing the object. Forexample, objects can have different relative dimensions and differentrelative locations on a wafer map. The object index thus provides amechanism for describing the object's dimensions, location, spatialpositioning, etc. relative to the wafer map. In some embodiments, anobject index can include, but is not limited to, an index defining ameasurement of the number of failure dies in the object (e.g. ageography length, a geography area, a geography width, a geographyheight), a centroid distance, a number of pixels, a distance or an anglerelative to a center or an edge of the wafer map or to some otherrelative fixed location of the wafer map, an object perimeter distance,a circumference, an inner circumference, an outer circumference, adifferential circumference, a diameter, a radius, a differential radius,a wafer map perimeter distance, a wafer map percent coverage, a distancefrom a fixed point on the wafer map to a centroid distance, a distancefrom a fixed point on the wafer map to a fixed point of the object, amajor axis length, a minor axis length, a bounding box, etc.

In various embodiments, an object index can be measured relative to thecenter of the wafer map. In some embodiments, an object index can bemeasured relative to some fixed point on the wafer map. In variousembodiments, the method can use, for example, standardized rectangularor polar coordinate systems, or other Euclidean coordinate system toenhance object selecting techniques. In various embodiments, an objectindex can define one or more respective attributes of an object. In someembodiments, an object index can define a respectiveposition-independent object related attribute. For example, an objectindex can define the shape or size of an object. In other embodiments,an object index can define a position-dependent object relatedattribute. For example, an object index can define the relative positionor percent coverage of the object relative to the wafer map.

According to various embodiments, one or more respective object indicesfor selecting a respective object on each of the respective selectedwafer maps are selected at block 115. For example, geography length andgeography area can be selected for selecting a respective object on arespective wafer map. By way of example, the one or more indices can beselected based on the dimensions of the objects on the wafer map.Further by way of example, the one or more indices can be selected basedon the spatial positioning of the objects relative to the wafer map.Further by way of example, a geography area and a perimeter distance maybe selected as indices for selecting a respective object on each of therespective selected wafer maps.

At block 120, a plurality of object index values in each respectiveselected wafer map are determined using each of the respective selectedone or more object indices. In various embodiments, the resultant valuesusing the one or more selected object indices are calculated for eachobject in each respective selected wafer map to select an object. Forexample, the resultant values for geography area and perimeter distanceare calculated for each object in each respective selected wafer map ofthe plurality of wafer maps to select an object.

At block 125, a respective object is selected in each respectiveselected wafer map based on the determined plurality of object indexvalues. For example, the object having the maximum object index valuesof geography area and perimeter distance can be selected. By way ofexample, the object having the minimum object index value (e.g.differential circumference between an outer circumference and an innercircumference) can be selected. In various embodiments, two or moreobjects can be selected in one or more of the respective selected wafermaps based on the determined plurality of object index values and storedobject index values for the selected one or more object indices.

With reference now to FIG. 2, a step of selecting an object 225 in amethod of automatically detecting failure patterns for a semiconductorwafer fabrication process according to some embodiments is provided. Atblock 227, two or more objects in one or more wafer maps of a pluralityof wafer maps can be selected. For example, stored object index values(block 226) may indicate that one object is an independent object andanother object is a dependent object such that there is a highprobability of both objects being observed on the same wafer map. By wayof example, object index values can be stored at block 226 in ahistogram format to indicate a relationship between the two or moreobjects. By way of another example, one object can have a maximumgeography area object index value and another object can have a maximumperimeter distance object index value. At block 228, the two or moreselected objects can be grouped to form a grouped object. For example,the objects can be merged and grouped to form one object and thenprocessed as an object as described below. In various embodiments, alinear discriminant analysis (LDA) can be performed to eliminate noiseduring the process of grouping or merging the two or more selectedobjects.

In some embodiments, a preliminary label and/or a recording label can beassigned to each object in each respective selected wafer map of theplurality of wafer maps. For example, a preliminary numeric label can beassigned to each object in each respective selected wafer map todifferentiate the objects during the selection process. In someembodiments, equivalences for the object index values can be determinedin a local equivalence table for the objects in each respective selectedwafer map of the plurality of wafer maps. In some embodiments, theequivalence classes can be resolved and the respective objects can berelabeled in each respective selected wafer map of the plurality ofwafer maps. For example, the object index values for a plurality ofobjects in a wafer map can be determined. The objects can bepreliminarily labeled to identify an object with its associated objectindex values. Two or more objects can be selected based on theirassociated object index values and stored object index values. Theobjects can be grouped, equivalences resolved for the determined objectindex values for the grouped object and a recording label can beassigned to the respective grouped object.

In various embodiments, one or more wafer map indices can be selectedfor determining a respective feature in each respective wafer map. Asused herein, a wafer map index for determining a respective feature ineach respective wafer map is a mechanism for describing the informationon the wafer map. In various embodiments, the selected one or moreobject indices are insufficient to describe the information on the wafermap for feature determination. For example, a wafer map index may be atransformation function applied to the failure data on the wafer map. Byway of example, a wafer map index may include, but is not limited to, aRadon transform, a Fourier transform, a Hough transform, a randomtransform, etc. A plurality of wafer map index values can be determinedin each respective wafer map using each respective selected one or morewafer map indices.

In some embodiments, a Radon transform can be selected as a wafer mapindex. A plurality of wafer map index values (e.g. s and α where srepresents a distance along any straight line L and α represents anangle that a straight line L makes with an axis) can be determined ineach respective wafer map such that the radon transform function returnsa radon transform R of an intensity image of the wafer map for eachangle α from a fixed point (e.g. center of the wafer map) in the wafermap. In various embodiments, determining a plurality of wafer map indexvalues can include determining a matrix of wafer map index values and aprojection of the wafer map index values. For example, determining aplurality of wafer map index values using a Radon transform wafer mapindex can involve computing a matrix in which each column is the Radontransform for one of the angles α from a fixed point (e.g. center of thewafer map) and computing an x-y projection where the y-axis representsthe distance (s) of the projection and relative to the fixed point andthe x-axis represents the angle α from 0 to 179 degrees.

At block 130, a respective feature in each of the respective selectedwafer maps is determined using the determined first plurality of objectindex values for the respective selected object and stored featureinformation. In various embodiments, feature information stored in adatabase at block 131 can include previously calculated object indexvalues determined using previously selected object indices in previouslyselected wafer maps. In some embodiments, feature information stored ina database at block 131 can include object index values object stored ina histogram format to indicate a relationship between two or moreobjects. In some embodiments, feature information stored in a databasecan include previously calculated object index values for groupedobjects in a previously selected wafer map. In some embodiments, thestored feature information can include feature points previouslydetermined for previous objects using previously selected object indicesin previously selected wafer maps and previously stored featureinformation.

For example, the geography length and geography area can be selected asobject indices in a respective wafer map. A plurality of object indexvalues can be calculated for a plurality of objects in each respectiveselected wafer map. Also, an object can be selected from the respectivewafer map based on the determined plurality of object index values. Invarious embodiments, the object index values for the selected object canbe combined (e.g. by plotting on a 2-D graph) to form a feature point.Then, the selected object can be compared with feature points stored inthe feature database. The feature database stores feature points withthe same selected object indices of each previously processed wafer. Onthe other hand, in various embodiments, a respective feature can bedetermined by using a predefined plurality of wafer map index values ineach selected wafer map. By way of example, a Radon transform functioncan be selected as a wafer map index. Then, the Radon transform functioncan be used as a plurality of wafer map index values for each respectiveselected wafer map. A feature point can be extracted for a respectivewafer map by selecting the maximum value of a Radon transform projectionfor the selected wafer map. In addition, a feature point can beextracted from a respective wafer map by averaging the projection matrixof the Random transform. After performing the average operation on theprojection matrix, then the more discriminating power 1-dimensional datacan be derived. This 1-dimensional data can be seen as a datadistribution of the selected wafer map. This feature also can becompared with other feature points in the feature database.

Further by way of example, geography area, geography length andperimeter distance can be selected as object indices for a respectivewafer map and a plurality of object index values can be calculated for aplurality of objects in each respective wafer map to select one or moreobjects in each respective selected wafer map. The resultant objectindex values for the respective selected one or more objects can becombined (e.g. by plotting on a 3-D graph) to form a feature point andcompared with feature points stored in a database for previous featuresof previously processed wafer maps formed using the same object indices.The feature point can be compared with a library of known feature pointsto match the feature point. In this manner, the quality of thedetermined feature can be quantified, and irrelevant features can bequickly eliminated. A respective determined feature for a respectivewafer map can include, but is not limited, to an upwardly curvingsemicircular feature (e.g. smile-shaped feature), a downwardly curvingsemicircular feature (e.g. frown shaped feature), a laterally curvingsemicircular feature, an elongated substantially linear featureemanating from the center of the wafer map, a circle of N units indiameter positioned substantially about the center of the wafer map, atriangle of N area units positioned in an upper left quadrant of thewafer map, etc.

Any suitable feature discrimination algorithm commonly used in the artto extract features in image processing can be utilized, oralternatively, future developed feature discrimination algorithms canalso be utilized. For example, degrees of correlation can be measuredbetween the determined feature point and previously stored featurepoints to extract the respective feature in the respective wafer map byselecting the feature point that has the highest degree of correlation.In some embodiments, a covariance matrix based distance (e.g. diagonalcovariance matrix based distance) can be determined for the featuredata. In various embodiments, a linear discriminant analysis (LDA) canbe performed for the feature data. Ranges for degrees of correlation canbe selected based on, for example, the feature discrimination algorithmselected for feature determination and/or based on the amount of failuredata in the respective selected wafer map and/or the amount of failuredata for a selected object in a respective selected wafer map. Forexample, a degree of correlation between approximately 70% and 100%(i.e. between 69.5% and 99.5%) between the determined feature point anda previously stored feature point can be used for featurediscrimination. In some embodiments, a degree of correlation rangebetween approximately 80% and 100% (i.e. between 79.5% and 99.5%)between the determined feature point and a previously stored featurepoint can be selected. In other embodiments, fuzzy matching can be usedand/or fuzzy membership degrees can be assigned to calculate the highestdegree of correlation and determine the respective feature. For example,a fuzzy matching algorithm can be used to compute and assignprobabilities of belonging to a certain feature to the newly determinedfeature point.

At block 140, a respective pattern for each of the respective selectedwafer maps is classified using the respective determined feature and aplurality of stored pattern information at block 161. A patternrepresents the one or more features on a respective wafer map,determined using the plurality of object index values and/or wafer mapindex values for the wafer map, and including the associated featureinformation, object index information and/or wafer map indexinformation, attribute information, object information and imageinformation on the wafer map (e.g. pixel information). Stored patterninformation can include pattern definitions and pattern templates.Pattern definitions can include feature information, object indexinformation and/or wafer map index information, attribute information,object information and image information on the wafer map (e.g. pixelinformation, angle information) for respective patterns. Patterntemplates can include a general format for pattern information in apattern (e.g. hierarchical relationship between feature information,attribute information, object information and image information). Invarious embodiments, a k-nearest neighbor algorithm is used to classifythe respective pattern for each of the respective selected wafer mapsusing the respective determined feature and a plurality of storedpattern information. Thus, a respective pattern is classified by amajority vote amongst its k nearest neighbors. The choice of k can be atrade-off between the variability and susceptibility to noise associatedwith a low value of k against the over-smoothing associated with a highvalue of k. In some embodiments, k can be determined by heuristictechniques commonly used in the art or alternatively by future developedtechniques that depend on the type of wafer map being processed, and/orthe number of defect data points being processed.

By way of example, a respective pattern for a respective selected wafermap can be classified using the formula Σ_(i,j∈Ni)d(xi, xj) where d(xi,xj)=(xi−xj)M(xi−xj), M is the identity matrix, that for an unknown wafermap classification point (x_(i), y_(i)) and N representing k, a distance(e.g. Euclidean or Mahalanobis distance) can be measured between theunknown wafer map classification point's independent variable value(x_(i)), the unknown wafer map classification point's dependent variablevalue (y_(i)), and k stored classification points (x_(j), y_(i)) forpreviously stored patterns. In some embodiments, (x_(i), y_(i)) can be afeature point for the determined feature for a respective wafer map. Inother embodiments, (x_(i), y_(i)) is determined based on two or morefeature points for a respective wafer map. For example, two or morefeature points for a respective wafer map can be combined (e.g. byplotting on a 2-D or 3-D graph) to form a classification point andcompared with classification points stored in a database 136 forclassified patterns of previously processed wafer maps formed using thesame two or more features. In various embodiments, classification pointscan be stored at block 136 for classified patterns including, but notlimited to, localized patterns, sector patterns, center patterns,scratch patterns, edge patterns, ring patterns, radiation patterns,donut patterns or top/bottom patterns.

For example, and with reference now to FIG. 6, k can be selected toequal 3 for classifying a respective pattern for each of a plurality ofrespective selected wafer maps. As described above, a respective wafermap can be determined to include respective feature 1 and respectivefeature 2 for a respective wafer map. The respective wafer map can bedetermined to include a classification point (x_(i)=determined feature 1point value, y_(i)=determined feature 2 point value). In variousembodiments, a respective wafer map can be determined to include onerespective feature and a classification point ((x_(i)=determined objectindex 1 point value, y_(i)=determined object index 2 point value). Therespective wafer map's respective pattern can be classified using a knearest neighbor algorithm (k=3), its respective classification pointand by majority voting among the classification points of two or more ofa plurality of stored classified patterns (x_(i)=stored feature 1 pointvalue, y_(i)=stored feature 2 point value) such as for example, centerpatterns and localized patterns. In the illustrated example, using a3-nearest neighbor algorithm, the respective pattern for the respectivewafer map is classified as a localized pattern. Once a respectivepattern for a respective wafer map is classified as described herein,the failure and/or defect type of the input wafer can be established. Invarious embodiments, the pattern information for each of the respectiveselected wafers is stored in pattern database 136. In some embodiments,the classified patterns are used to adjust one or more parameters of thesemiconductor fabrication process.

At block 140, a respective wafer fingerprint for each of the respectiveselected wafer maps is formed using the respective classified patternand the plurality of stored patterns. In various embodiments, eachrespective classified pattern represents a respective wafer fingerprint,corresponding to a classified failure type, and having its respectivedetermined feature information, object index information and/or wafermap index information, attribute information, object information andimage information. In various embodiments, each respective waferfingerprint is stored in a database. For example, each respective waferfingerprint can be stored in a wafer fingerprint library.

In various embodiments, the plurality of respective classified patternscan be ranked using a clustering algorithm with stored patterns ofsubstantially similar classification (e.g. localized, sector, center,scratch, edge, ring, radiation, donut or top/bottom.) In variousembodiments, a hierarchical clustering method can be used to form arespective wafer fingerprint for each of the respective selected wafermaps. For example, a respective wafer fingerprint can be formed for eachof the respective selected wafer maps using the formula D_(max)(C_(i),_(j))=max d(a,b) where α∈Ci, b∈Cj where d represents a distance (e.g.Euclidean distance) between i and j, two points in pattern clustersC_(i) and C_(j) and D_(max) represents the cluster distance. In thisalgorithm, the distance of the farthest points of the two clusters canbe compared. In other embodiments, a respective wafer fingerprint can beformed for each of the respective selected wafer maps using the formulaD_(min)(C_(i), C_(j))=min d(a,b) where α∈Ci, b∈Cj where d againrepresents a distance (e.g. Euclidean distance) between i and j, twopoints in pattern clusters C_(i) and C_(j) and D_(min) represents thecluster distance. In this algorithm, the distance of the closest pointsof the two clusters can be compared. If the computed cluster distance ineither algorithm is below a certain predetermined threshold, then thetwo clusters are completely merged. In various embodiments, an averagevalue is determined in each pattern cluster C_(i) and C_(j) and theaverage value represents the cluster centroid. In various embodiments,the cluster distance (d) is computed between the cluster centroids ofpattern clusters C_(i) and C_(j).

Each of the newly classified patterns and each of the stored patternscan be individually assigned to their own pattern cluster. The closest(most similar, e.g. closest distance) pair of pattern clusters aremerged into a single pattern cluster, so that one less pattern clusterexists. The distances between the new cluster and each of the remainingpattern clusters is computed and the two closest (most similar) patternclusters are again merged into a single pattern cluster, The algorithmcan then iterate until no more merging occurs, at which point allpattern clusters are formed. The pattern information of the formedpattern clusters represent new wafer failure patterns. A waferfingerprint is thus formed that represents the new wafer failurepattern. The wafer fingerprint can be stored in a database such as, forexample, a wafer fingerprint library.

At block 145, the respective wafer fingerprints are used to adjust oneor more parameters of the semiconductor fabrication process. Forexample, the one or more parameters can include active processparameters, passive process parameters, design parameters, layoutparameters, or combinations thereof. Further, by way of example, anactive (or actively controlled) process parameter can include anyprocess parameter that can be easily specified during a particularsemiconductor process (such as, by defining the parameter in anequipment recipe.) Examples of an active parameter include an RF power,a gas flow rate, a concentration, a trim time, a pressure, or aprocessing time. A passive parameter can include any fabrication processparameter that is not determined by recipe but rather, for example, is adependent variable inherent in a process based upon other passive and/oractively controlled parameters, a type of equipment, a condition ofequipment, a condition of a wafer being processed, and/or other possiblefactors. Examples of a passive process parameter include reflectedpower, ambient conditions, contaminate levels, and temperature and/orpressure profiles inherent in a fabrication tool. Further examplesinclude critical dimension (CD) or depth for a poly etching process. Invarious embodiments, a respective classified pattern, and its associatedwafer fingerprint, can be used in identifying a process within a toolthat caused the indicated failure type. For example, a respectiveclassified pattern may indicate a problem with a deposition process, ora chemical dispensing process, or a wafer handling or transfer process,or a problem with a photo-lithography tool. The inventors havedetermined that the use of the determined wafer fingerprints cansignificantly reduce this analysis and identification time and thussignificantly improve yield. A process parameter associated with anidentified problem, for example with a wafer handling or transferprocess, can be adjusted accordingly. In various embodiments, aplurality of semiconductor wafers are fabricated in a wafer fabricationprocess incorporating the adjusted process parameter.

FIGS. 3A-3D illustrate simplified diagrams of examples of wafer mapsaccording to various embodiments of the present disclosure. Withreference now to FIG. 3A, a plurality of wafer maps are formed based ona test data set collected from testing a plurality of semiconductorwafers as described above for blocks 102-106 for method 100. Asillustrated in FIG. 3B, a determination is made as to whether eachrespective wafer map includes one or more respective objects asdescribed above at block 110. On the left of the dashed line, foursimplified diagrams of examples of wafer maps are depicted, eachincluding one or more respective objects. On the right of the dashedline, three simplified diagrams of examples of wafer maps are depicted,where each wafer map does not include one or more respective objects.With reference now to FIG. 3C, one of the three simplified diagrams ofwafer maps of FIG. 3B is shown. In the illustrated embodiment, therespective example of a wafer map is determined to include three objects(310, 320, 330). In various embodiments, as described above, therespective example of a wafer map can be segmented by selected objectswhere each selected object in the wafer map is individually processed asdescribed above. As shown, the respective wafer map having threeselected objects can be split into three sub-wafer maps, each containinga subset of the failure data for a particular wafer map.

FIG. 3D shows the three simplified diagrams of examples of sub-wafermaps of FIG. 3C are shown. As described above for block 115, two or moreobject indices in each of the sub-wafer maps can be selected. As shownin the example illustrated on the left side of FIG. 3D, an index 1 ofgeography width and an index 2 of geography length can be selected and aplurality of object index values for the objects illustrated in thesub-wafer map depicted therein can be determined. As shown in the centerexample of FIG. 3D, a plurality of object index values can be determinedusing selected object indices 1 and 2 for the objects illustrated in thesub-wafer map depicted therein. In some embodiments, the selected objectindices can differ for each sub-wafer map. As shown in FIG. 3D, theobject indices can be identical for each sub-wafer map. As shown in thesub-wafer map illustrated on the right side of FIG. 3D, a plurality ofobject index values can be determined using selected object indices 1and 2 for the objects illustrated therein.

FIGS. 3E and 3F illustrate diagrams showing examples of a step offeature determination using a pair of object indices and stored featureinformation according to embodiments of the present disclosure. Asdescribed above for block 135 and illustrated in FIGS. 3E and 3F, theresultant plurality of object index values for the selected objects inthe respective sub-wafer maps can be calculated and combined (e.g. byplotting on a 2-D graph) to form a feature point and compared withfeature points stored in a database for previous features of previouslyprocessed wafer maps formed using the same respective object indices. Invarious embodiments (not shown), three object indices can be used forthe step of feature determination. In some embodiments (not shown), oneor more wafer map indices can be used for the step of featuredetermination.

Referring now to FIG. 4A, a simplified diagram of a wafer map 400according to various embodiments is provided. As described above atblock 110, wafer map 400 is determined to comprise one or morerespective objects (410, 415, 420, 425, 430, 435, 440, 445, 450, 455,460, 470, 475, 480, 485). One or more object indices can be selected toselect a respective object (410, 415, 420, 425, 430, 435, 440, 445, 450,455, 460, 470, 475, 480, 485) in the respective selected wafer map 400.In the illustrated embodiment, the selected object indices are geographyarea and perimeter distance. Referring now to FIG. 4B, a simplifiedchart of determined plurality of object index values for each of therespective objects (410, 415, 420, 425, 430, 435, 440, 445, 450, 455,460, 470, 475, 480, 485) in the respective selected wafer map 400 isshown. In the example illustrated in FIG. 4B, the resultant values ofgeography area (index 1) and perimeter distance (index 2) are shown in atabular format. Object 410 has the largest geography area and perimeterdistance. In various embodiment, object 410 is selected based on thedetermined plurality of object index values shown in FIG. 4B. FIG. 4Cillustrates a diagram showing a step of feature determination usingobject index values for object 410 and stored feature information. Theresultant determined object index values for geography area andperimeter distance for selected object 410 can be combined (e.g. byplotting on a 2-D graph) to form a feature point and can then becompared with feature points stored in a database for previous featuresof previously processed wafer maps formed using the same respectiveobject indices (geography area and perimeter distance). FIG. 4C showsthat a feature of wafer map 400 is determined using the feature point ofobject 410 and stored feature information.

FIG. 5 are simplified diagrams showing stored classified patterninformation for various pattern templates according to some embodiments.As described above at block 136, a plurality of classified patterns andpattern information are stored to permit classification of respectivepatterns for respective selected wafer maps using a respective featuredetermined therein. As described above and as illustrated in FIG. 4,classification points and other pattern information can be stored atblock 136 for classified patterns including, but not limited to,localized patterns, sector patterns, center patterns, scratch patterns,edge patterns, ring patterns, radiation patterns, donut patterns ortop/bottom patterns. FIG. 5 illustrates examples of stored patterntemplates and pattern definitions generally used to classify respectivepatterns for respective selected wafer maps using the respective featuredetermined therein.

Referring now to FIG. 7, a flow chart illustrating a step of featuredetermination 730 using a pair of object indices and stored featureinformation for a plurality of stored patterns according to variousembodiments is provided. The same one or more object indices forselecting an object in two or more of the plurality of wafer maps can beselected and an object is selected in the two or more of the pluralityof selected wafer maps as described above. At block 732, a respectivefeature point is determined using the object index values determined forthe selected object in each respective wafer map. At block 732, acomparison is made between the stored feature information and therespective determined feature point in each of the respective selectedwafer maps. By way of example, a feature point can be determined in eachof the respective selected wafer maps and compared to stored featurepoints. If a plurality of determined feature points have a higher degreeof correlation with each other than with any of the plurality of storedfeature points, than a new feature is determined in the respectiveselected wafer maps having the plurality of determined feature points.The information associated with the determined new feature, including,for example, the new feature point, can be stored in a database orlibrary with other previously stored feature information.

FIG. 8 illustrates a diagram showing a step of new feature determinationusing a pair of object indices and stored feature information accordingto an example. The same object indices (Index 1 and Index 2) areselected in two or more of the plurality of wafer maps and werepreviously selected in prior wafer maps containing the stored featureinformation (block 731). As illustrated, a new feature pointcorresponding to a new feature can be determined if the determinedfeature points have a higher degree of correlation with each other thanwith any of the plurality of stored feature points for a plurality ofstored patterns. This method permits determining degrees of similaritybetween selected wafer maps using determined and stored featureinformation. In various embodiments, a new feature can be determinedusing stored feature information (e.g. feature points) and determinedfeature information (e.g. feature points) by determining a degree ofcorrelation between them.

Referring now to FIG. 9, a flow chart illustrating a step of new patterndetermination of a method of automatically detecting failure patternsfor a semiconductor wafer fabrication process according to someembodiments is provided. One or more respective features in a pluralityof wafer maps is determined as described above. At block 937, aplurality of respective patterns for the plurality of wafer maps areclassified as described above for block 135 using stored patterninformation at block 936. At block 938, a plurality of the respectiveclassified patterns are grouped into two or more pattern clusters. Invarious embodiments, a hierarchical clustering method or agglomerativeclustering method can be used to merge the two or more pattern clustersinto a single pattern cluster as described above. At block 939, the twoor more clusters are used in connection with a clustering algorithm toform new patterns and pattern information for storing in a library ordatabase.

In some embodiments, various steps of the method can be implemented by ageneral purpose computer programmed in accordance with the principalsdiscussed herein. Certain features that are described in thisspecification in the context of separate embodiments can also beimplemented in combination in a single embodiment. Conversely, variousfeatures that are described in the context of a single embodiment canalso be implemented in multiple embodiments separately or in anysuitable subcombination. Moreover, although features can be describedabove as acting in certain combinations and even initially claimed assuch, one or more features from a claimed combination can in some casesbe excised from the combination, and the claimed combination can bedirected to a subcombination or variation of a subcombination.

Similarly, while operations are depicted in the drawings in a particularorder, this should not be understood as requiring that such operationsbe performed in the particular order shown or in sequential order, orthat all illustrated operations be performed, to achieve desirableresults. In certain circumstances, multitasking and parallel processingcan be advantageous. Moreover, the separation of various systemcomponents in the embodiments described above should not be understoodas requiring such separation in all embodiments, and it should beunderstood that the described program components and systems cangenerally be integrated together in a single software product orpackaged into multiple software products.

Processes and logic flows described in this specification can beperformed by one or more programmable processors executing one or morecomputer programs to perform functions by operating on input data andgenerating output. The processes and logic flows can also be performedby, and apparatus can also be implemented as, special purpose logiccircuitry, e.g., an FPGA (field programmable gate array) or an ASIC(application specific integrated circuit).

A diagram of an illustrative example of an architecture of a computerprocessing unit according to some embodiments is shown in FIG. 10.Embodiments of the subject matter and the functional operations forvarious steps of processes described in this specification can beimplemented in electronic circuitry, or in computer firmware, orhardware, including the structures disclosed in this specification andtheir equivalents, or in combinations of one or more of them.Embodiments of the subject matter described in this specification can beimplemented as one or more computer program products, i.e., one or moremodules of computer program instructions encoded on a tangible machinereadable storage medium for execution by, or to control the operationof, data processing apparatus. The tangible storage medium can be acomputer readable medium. The computer readable medium can be amachine-readable storage device, a machine-readable storage substrate, amemory device, a hard disk drive, a tape drive, an optical drive (suchas, but not limited to CDROM, DVD, or BDROM) or the like, or acombination of one or more of them.

At least a portion of the system and method for automatically detectingfailure patterns for a semiconductor wafer fabrication process describedherein can be implemented in computer processing unit 1000 andspecifically in software and where results (e.g. indices, featureinformation, attributes, pattern information), and/or system parameterscan be presented to system operator on a graphical user interface (GUI)on a display device such as a computer monitor 1024 (1026) or otherdisplay device. Embodiments of the subject matter described in thisspecification can be implemented on a computer 1000 having a keyboard,pointing device, e.g., a mouse or a trackball, by which the operator canprovide input to the computer. Other kinds of devices can be used toprovide for interaction with an operator as well; for example, inputfrom the operator can be received in any form, including acoustic,speech, or tactile input. In various embodiments, the computer system1000 includes functionality providing for various components of thesystems for automatically detecting failure patterns for a semiconductorwafer fabrication process and steps of the corresponding methods asdescribed, for example, in FIGS. 1-9.

As illustrated in FIG. 10, computer processing unit 1000 can include oneor more processors 1002. The processor 1002 is connected to acommunication infrastructure 1006 (e.g., a communications bus,cross-over bar, or network). Computer processing unit 1000 can include adisplay interface 1022 that forwards graphics, text, and other data fromthe communication infrastructure 1006 (or from a frame buffer not shown)for display on the display unit 1024. Computer processing unit 1000 caninclude a warning indication interface (not shown) that forwards warningindicators and other data from the communication infrastructure 1006 (orfrom a frame buffer not shown) to a warning indicator (not shown).

Computer processing unit 1000 can also include a main memory 1004, suchas a random access memory (RAM), and a secondary memory 1008. Thesecondary memory 1008 can include, for example, a hard disk drive (HDD)1010 and/or removable storage drive 1012, which can represent a floppydisk drive, a magnetic tape drive, an optical disk drive, or the like.The removable storage drive 1012 reads from and/or writes to a removablestorage unit 1016. Removable storage unit 1016 can be a floppy disk,magnetic tape, optical disk, or the like. As will be understood, theremovable storage unit 1016 can include a computer readable storagemedium having stored therein computer software and/or data. Computerreadable storage media suitable for storing computer programinstructions and data include all forms data memory includingnonvolatile memory, media and memory devices, including by way ofexample semiconductor memory devices, e.g., EPROM, EEPROM, and flashmemory devices; magnetic disks, e.g., internal hard disks or removabledisks; magneto optical disks; and CD ROM, DVD-ROM, and BDROM disks. Theprocessor 1002 and the memory 1004 can be supplemented by, orincorporated in, special purpose logic circuitry.

In alternative embodiments, secondary memory 1008 can include othersimilar devices for allowing computer programs or other instructions tobe loaded into computer processing unit 1000. Secondary memory 1008 caninclude a removable storage unit 1018 and a corresponding interface1014. Examples of such removable storage units include, but are notlimited to, USB or flash drives, which allow software and data to betransferred from the removable storage unit 1018 to computer processingunit 1000.

Computer processing unit 1000 can also include a communicationsinterface 1020. Communications interface 1020 allows software and datato be transferred between computer processing unit 1000 and externaldevices. Examples of communications interface 1020 can include a modem,Ethernet card, wireless network card, a Personal Computer Memory CardInternational Association (PCMCIA) slot and card, or the like. Softwareand data transferred via communications interface 1020 can be in theform of signals, which can be electronic, electromagnetic, optical, orthe like that are capable of being received by communications interface1020. These signals can be provided to communications interface 1020 viaa communications path (e.g., channel), which can be implemented usingwire, cable, fiber optics, a telephone line, a cellular link, a radiofrequency (RF) link and other communication channels.

The computer program products provide software to computer processingunit 1000. Computer programs (also referred to as computer controllogic) are stored in main memory 1004 and/or secondary memory 1008.Computer programs can also be received via communications interface1020. Such computer programs, when executed by a processor, enable thecomputer system 1000 to perform features of the method discussed herein.For example, main memory 1004, secondary memory 1008, or removablestorage units 1016 or 1018 can be encoded with computer program code forperforming various steps of the processes described in FIGS. 1-9.

In an embodiment implemented using software, the software can be storedin a computer program product and loaded into computer processing unit1000 using removable storage drive 1012, hard drive 1010, orcommunications interface 1020. The software, when executed by aprocessor 1002, causes the processor 1002 to perform the functions ofvarious steps of the methods described herein. In another embodiment,various steps of the methods can be implemented primarily in hardwareusing, for example, hardware components such as a digital signalprocessor comprising application specific integrated circuits (ASICs).In yet another embodiment, the method is implemented using a combinationof both hardware and software.

Various embodiments can be implemented in a computing system thatincludes a back end component, e.g., as a data server, or that includesa middleware component, e.g., an application server, or that includes afront end component, e.g., a computer having a GUI or a Web browserthrough which an operator can interact with an implementation of thesubject matter described is this specification, or any combination ofone or more such back end, middleware, or front end components. Thecomponents of the system can be interconnected by any form or medium ofdigital data communication, e.g., a communication network. Examples ofcommunication networks include a local area network (“LAN”) and a widearea network (“WAN”), e.g., the Internet.

Many of the steps described above are adapted to be performed using aprogrammed processor. For example, FIG. 11 shows a processor 1102programmed to perform the method steps. The steps can be performed by aplurality of separate computer programs, or by a program that promptsthe user for additional inputs between and/or during steps. These stepscan include one or more of the receiving a test data set collected fromtesting a plurality of fabricated semiconductor wafers 1111, forming arespective wafer map for each wafer of the plurality of wafers 1112,wafer map object determination 1113, wafer map selection 1114, objectindex for selecting a respective object selection 1115, object indexvalue determination 1116, object selection 1117; feature determination1118, pattern classification 1119, wafer fingerprint formation 1120 andinterface for wafer fingerprint utilization to adjust one or moreparameters of the semiconductor fabrication process 1121. In someembodiments, all numerically intensive calculations are performed by aprogrammed processor, and the results presented to the user (e.g.,engineer) at certain decision points to allow input of engineeringjudgment. Although FIG. 11 shows a single processor, in otherembodiments, various subsets of the processes 1111-1121 can be executedon a plurality of programmed processors, which can optionally beconnected to each other by a communications network, such as a personalarea network, a local area network, a wide area network, and/or theInternet.

Referring now to FIG. 12, an example of a system 1200 for automaticallydetecting failure patterns for a semiconductor wafer process is shown. Amonitoring device 1205 is provided to monitor a plurality ofsemiconductor wafers for failure test data. A data determining device1210 is shown to retrieve a failure test data set for each semiconductorwafer of the plurality of semiconductor wafers from the monitoredfailure test data. A wafer map generating device 1215 is shown to form arespective wafer map for each semiconductor wafer of the plurality ofsemiconductor wafers based on the respective retrieved failure test dataset. In some embodiments, one or more of the monitoring device 1205,data determining device 1210 or wafer map generating device 1215 can beprovided external to a processor 1100 or other system components forperforming one or more of the other steps for automatically detectingfailure patterns for a semiconductor wafer process. As shown, aplurality of wafer maps are provided from wafer map generating device1215 to an index determining device 1220 to determine one or morerespective object indices for selecting an object in each of therespective wafer maps. A first data determining device 1225 is includedto determine a plurality of object index values in each respectiveselected wafer map using each of the respective selected one or moreobject indices received from index determining device 1220. A database1230 is included to store feature information and pattern informationfor a plurality of patterns and to provide the stored information toother components of system 1200. A second data determining device 1235is shown to determine a respective feature in each of the respectivewafer maps using a respective selected object in each respective wafermap, the determined plurality of object index values for a respectiveselected object received from the first data determining device 1225 andstored feature information received from database 1230.

Data classifying device 1240 is provided to classify a respectivepattern for each of the respective wafer maps using the respectivedetermined feature received from second data determining device 1235 andthe stored pattern information received from database 1230. A generator1245 is included in system 1200 to generate a respective waferfingerprint for each of the respective wafer maps using the respectiveclassified pattern received from data classifying device 1240 and thestored pattern information received from database 1230. The illustratedsystem 1200 also includes an interface 1250 to provide the respectivewafer fingerprints to a controller 1260 to adjust one or more parametersof the semiconductor fabrication process. A wafer fabrication device(not shown) can receive an input from controller 1260 to fabricate aplurality of semiconductor wafers using the adjusted one or moreparameters of the semiconductor fabrication process. In variousembodiments, database 1230 can receive wafer fingerprint informationfrom generator 1245, object index information from first datadetermining device 1225, feature information from second datadetermining device 1235, and/or pattern information from dataclassifying device 1240. In some embodiments, system 1200 can include acomparator (not shown) to compare a respective determined feature pointin each respective wafer map with a plurality of stored feature pointsfor a plurality of stored patterns wherein the second data determiningdevice 1235 is further configured to determine the respective featurepoint in each respective wafer map using the determined plurality ofobject index values. In some embodiments, the system 1200 can include athird data determining device (not shown) to determine a new feature inone or more of the respective selected wafer maps if a plurality ofdetermined feature points received from the second data determiningdevice 1235 have a higher degree of correlation with each other thanwith any of the plurality of stored feature points for the plurality ofstored patterns received from database 1230. In some embodiments,database 1230 can store information associated with the determined newfeature. In other embodiments, a separate database (not shown) can storeinformation associated with the determined new feature.

One embodiment provides a method of automatically detecting failurepatterns for a semiconductor wafer process. The method includesreceiving a test data set collected from testing a plurality ofsemiconductor wafers. A respective wafer map is formed for each of thewafers based on the collected test data set. The method also includesdetermining whether each respective wafer map comprises one or morerespective objects. The wafer maps that are determined to comprise oneor more respective objects are selected. One or more object indices forselecting a respective object in each respective selected wafer map areselected. The method further includes determining a plurality of objectindex values in each respective selected wafer map using each of therespective selected one or more object indices. An object in eachrespective selected wafer map is selected based on the determinedplurality of object index values. A respective feature in each of therespective selected wafer maps is determined using the respectiveselected object in each respective selected wafer map, the determinedplurality of object index values for the respective selected object, andstored feature information. A respective pattern for each of therespective selected wafer maps is classified using the respectivedetermined feature and stored pattern information for a plurality ofstored patterns. A respective wafer fingerprint is formed for each ofthe respective selected wafer maps using the respective classifiedpattern and the stored pattern information. The respective waferfingerprints are used to adjust one or more parameters of thesemiconductor fabrication process.

Another embodiment provides a computer readable storage medium havinginstructions stored tangibly thereon, the instructions when executed bya processor cause the processor to perform the operations of: receivinga test data set collected from testing a plurality of semiconductorwafers, forming a respective wafer map for each of the wafers based onthe collected test data set, determining whether each respective wafermap comprises one or more respective objects, selecting the wafer mapsthat are determined to comprise one or more respective objects,selecting one or more object indices for selecting a respective objectin each respective selected wafer map, determining a plurality of objectindex values in each respective selected wafer map using each of therespective selected one or more object indices, selecting an object ineach respective selected wafer map based on the determined plurality ofobject index values, determining a respective feature in each of therespective selected wafer maps using the respective selected object ineach respective selected wafer map, the determined plurality of objectindex values for the respective selected object, and stored featureinformation; classifying a respective pattern for each of the respectiveselected wafer maps using the respective determined feature and storedpattern information for a plurality of stored patterns, forming arespective wafer fingerprint for each of the respective selected wafermaps using the respective classified pattern and the stored patterninformation and using the respective wafer fingerprints to adjust one ormore parameters of the semiconductor fabrication process.

A further embodiment provides a system for automatically detectingfailure patterns for a semiconductor wafer process. The system includesa monitoring device to monitor a plurality of semiconductor wafers forfailure test data. A data retrieval device is included to retrieve afailure test data set for each semiconductor wafer of the plurality ofsemiconductor wafers from the monitored failure test data. A wafer mapgenerating device is included to form a respective wafer map for eachsemiconductor wafer of the plurality of semiconductor wafers based onthe respective retrieved failure test data set. The system also includesan index determining device to determine one or more object indices forselecting a respective object in each of the respective wafer maps. Afirst data determining device is provided to determine a plurality ofobject index values in each respective selected wafer map using each ofthe respective selected one or more object indices. A database isincluded to store feature information and pattern information for aplurality of patterns. A second data determining device is included todetermine a respective feature in each of the respective wafer mapsusing a respective selected object in each respective wafer map, thedetermined plurality of object index values for the respective selectedobject, and stored feature information. The system further includes adata classifying device to classify a respective pattern for each of therespective wafer maps using the respective determined feature and thestored pattern information, a generator to generate a respective waferfingerprint for each of the respective wafer maps using the respectiveclassified pattern and the stored pattern information, and an interfaceto provide the respective wafer fingerprints to a controller to adjustone or more parameters of the semiconductor fabrication process.

While various embodiments have been described, it is to be understoodthat the embodiments described are illustrative only and that the scopeof the subject matter is to be accorded a full range of equivalents,many variations and modifications naturally occurring to those of skillin the art from a perusal hereof.

What we claim is:
 1. A method of fabricating semiconductor wafers usingautomatically detected failure patterns, comprising: identifying asemiconductor wafer fabrication process or tool requiring adjustmentusing respective wafer fingerprints formed for each of a plurality ofwafer maps comprising one or more respective objects.
 2. The method ofclaim 1, further comprising: adjusting the identified semiconductorwafer fabrication process or tool using the respective waferfingerprints.
 3. The method of claim 2, further comprising: fabricatinga plurality of semiconductor wafers using the adjusted identifiedsemiconductor wafer fabrication process or tool.
 4. The method of claim1, wherein the step of identifying comprises identifying thesemiconductor wafer fabrication process or tool requiring adjustmentusing a respective wafer fingerprint and its corresponding respectiveclassified pattern.
 5. The method of claim 1, further comprising:adjusting a parameter of the identified semiconductor wafer fabricationprocess or tool using the respective wafer fingerprints.
 6. The methodof claim 5, wherein the parameter is selected from the group consistingof: active process parameters, passive process parameters, designparameters, layout parameters, and combinations thereof.
 7. The methodof claim 1, wherein the identified semiconductor wafer fabricationprocess or tool is a deposition, chemical dispensing, wafer handling,wafer transfer, or photo-lithography process or tool.
 8. The method ofclaim 1, wherein the respective wafer fingerprints are formed by:selecting an object in each respective wafer map of the plurality ofwafer maps; determining a respective feature in each of therespective-wafer maps using the respective selected object and storedfeature information; classifying a respective pattern for each of therespective-wafer maps using the respective determined feature and storedpattern information for a plurality of stored patterns; and using therespective classified pattern and the stored pattern information to formthe respective wafer fingerprints.
 9. The method of claim 8, wherein therespective wafer fingerprints are further formed by: selecting one ormore object indices for selecting objects; and determining a pluralityof object index values in each respective wafer map using the selectedone or more object indices; wherein the step of selecting an object ineach respective wafer map is based on the determined plurality of objectindex values.
 10. The method of claim 9, wherein the step of determininga respective feature in each of the respective wafer maps furthercomprises determining a respective feature point using the determinedplurality of object index values and a plurality of stored featurepoints for a plurality of stored patterns.
 11. A method of fabricatingsemiconductor wafers using automatically detected failure patterns,comprising: adjusting one or more parameters of a semiconductor waferfabrication process or tool using respective wafer fingerprints formedfor each of a plurality of wafer maps comprising one or more respectiveobjects.
 12. The method of claim 11, further comprising: fabricating aplurality of semiconductor wafers using the adjusted one or moreparameters of the semiconductor wafer fabrication process or tool. 13.The method of claim 11, wherein the one or more parameters are selectedfrom the group consisting of: active process parameters, passive processparameters, design parameters, layout parameters, and combinationsthereof.
 14. The method of claim 11, wherein the respective wafer mapsare formed by: determining a plurality of object index values in eachrespective wafer map; selecting an object in each respective wafer mapusing the determined plurality of object index values; determining arespective feature in each of the respective wafer maps using therespective selected object and stored feature information; classifying arespective pattern for each of the respective wafer maps using therespective determined feature and stored pattern information for aplurality of stored patterns; and using the respective classifiedpattern and the stored pattern information to form the respective waferfingerprints.
 15. The method of claim 14, wherein the respective waferfingerprints are further formed by: selecting one or more object indicesfor selecting objects in each respective wafer map; wherein the step ofdetermining a plurality of object index values in each respective wafermap further comprises using the respective selected one or more objectindices; and wherein the step of determining a respective feature ineach of the respective wafer maps further comprises using the determinedplurality of object index values for the respective selected object. 16.A system for fabricating semiconductor wafers using automaticallydetected failure patterns, comprising: a controller to adjust one ormore parameters of a semiconductor wafer fabrication process or toolusing respective wafer fingerprints provided to the controller via aninterface for each of a plurality of wafer maps comprising one or morerespective objects.
 17. The system of claim 16, further comprising: awafer fabrication device to fabricate a plurality of semiconductorwafers using the adjusted one or more parameters of the semiconductorfabrication process or tool.
 18. The system of claim 16, furthercomprising: a first data determining device to determine a respectivefeature in each of the plurality of wafer maps using a respectiveselected object in each respective wafer map and stored featureinformation; a data classifying device to classify a respective patternfor each of the respective wafer maps using the respective determinedfeature and stored pattern information; and a generator to generate therespective wafer fingerprints using the respective classified patternsand the stored pattern information.
 19. The system of claim 18, furthercomprising: an index determining device to determine one or more objectindices for selecting a respective object in each respective wafer map;and a second data determining device to determine a plurality of objectindex values in each respective wafer map using the determined one ormore object indices.
 20. The system of claim 19, wherein the first datadetermining device is further to determine a respective feature in eachof the plurality of wafer maps using a respective selected object ineach respective wafer map, the determined plurality of object indexvalues for the respective selected object, and stored featureinformation.